\frame{ \frametitle{Conclusion and Future Work}
  \begin{itemize}
    \item Absence of labelled data in the target domain undermines
          the accuracy of the domain transformation, resulting in
          $<50\%$ accuracy for most unsupervised source-target domain
          pairs.
    \item However, this is a novel method that uses a physically
          meaningful transformation.  It would be worthwhile to:
    \begin{itemize}
       \item Utilitize generic priors on possible domain shifts; and
       \item Explore data representations beyond linear subspaces
             particularly ones for which domain-invariant properties
             could be utilized.
    \end{itemize} 
  \end{itemize} 
}
